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An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings

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Listed:
  • Png, Ethan
  • Srinivasan, Seshadhri
  • Bekiroglu, Korkut
  • Chaoyang, Jiang
  • Su, Rong
  • Poolla, Kameshwar

Abstract

Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant’s thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs.

Suggested Citation

  • Png, Ethan & Srinivasan, Seshadhri & Bekiroglu, Korkut & Chaoyang, Jiang & Su, Rong & Poolla, Kameshwar, 2019. "An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings," Applied Energy, Elsevier, vol. 239(C), pages 408-424.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:408-424
    DOI: 10.1016/j.apenergy.2019.01.229
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    References listed on IDEAS

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    1. Radhakrishnan, Nikitha & Su, Yang & Su, Rong & Poolla, Kameshwar, 2016. "Token based scheduling for energy management in building HVAC systems," Applied Energy, Elsevier, vol. 173(C), pages 67-79.
    2. Okochi, Godwine Swere & Yao, Ye, 2016. "A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 784-817.
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    Cited by:

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    2. Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
    3. Karam M. Al-Obaidi & Mohataz Hossain & Nayef A. M. Alduais & Husam S. Al-Duais & Hossein Omrany & Amirhosein Ghaffarianhoseini, 2022. "A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective," Energies, MDPI, vol. 15(16), pages 1-32, August.
    4. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    5. Kim, Jeong Hun & Cho, Jae Yong & Jhun, Jeong Pil & Song, Gyeong Ju & Eom, Jong Hyuk & Jeong, Sinwoo & Hwang, Wonseop & Woo, Min Sik & Sung, Tae Hyun, 2021. "Development of a hybrid type smart pen piezoelectric energy harvester for an IoT platform," Energy, Elsevier, vol. 222(C).
    6. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    7. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
    8. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    9. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    10. Adrian Chojecki & Arkadiusz Ambroziak & Piotr Borkowski, 2023. "Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort," Energies, MDPI, vol. 16(7), pages 1-21, March.
    11. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    12. Raman, Naren Srivaths & Chen, Bo & Barooah, Prabir, 2022. "On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study," Applied Energy, Elsevier, vol. 324(C).
    13. Rafsanjani, Hamed Nabizadeh & Ghahramani, Ali & Nabizadeh, Amir Hossein, 2020. "iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings," Applied Energy, Elsevier, vol. 266(C).

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